I want to create an android

The AI and robotics community reccomends donating your own brain for the project. You are such an intelligent person, if you transplant your brain into the robot, you will have a superior product.

Well who knows? If your level of knowledge in robotics enables you to think that a "Star Trek - Data" type android is possible with current technology, perhaps you should tell us.

It is not possible with the current state of human knowledge. If it will ever be possible, it will be far in the future. It is not even known for sure if a digital computer can have a mind like a human, although many members of the AI community believe this as an act of faith. For an excellent presentation of a different view see Roger Penrose's book "The Emperors New Mind".

Mitchell Timin

well, AI is not able to create anything like that now but when we give birth to AI, i think the first machine compareble to "startreks data" will be created with techniques heavely inspired on the 'process of creation' of ourselves. So my guess it would be something like an EANN (evolutionary artificiall neural network) based on principles from molecular cell biology. eggenberger-hotz is developing EANN's.

if its possible to create 'data' on a symbolic level i think it would be much later.

and dont pay any attention to these spare-time philosophers just remember: they laughed at the wright brother but the also laughed at bozo the clown

Hello Craig, here is an interesting site that you might enjoy:

Dave

snipped-for-privacy@msn.com wrote:

Check to see if the positronic brain has been invented yet.

The solution will not be as simple as a neural network. Even if it has the same number of neuron as the brain.

Come back in 50 years time and ask again.

Since you bring up ANNs and Evolution, let me call attention to the URL below, which is devoted to that. There is software there, written in C, that you can download at no charge.

Mitchell Timin

Thanks I. Myself,

I look forward to trying out your url.

Craig

you question is naive and unsophisticated.

you pose a question that is on the edge or beyond our presence science (at least in the open literature domain)

for clues as others have said study Penrose and wait about 50 years

The answer is obvious, IMO. Since the only intelligence we know of is biological intelligence and since biological intelligence is made of a number of integregated cell assemblies (neural subnetworks), it follows that future androids will use integrated artificial neural subnetworks as well. However, these subnetworks will bear little ressemblance with current ANNs. For one, biological networks are discrete signal processors, i.e., they process neural spikes. It is an inherently temporal phenomenon.

Why neural networks, you ask? Because it is the only paradigm that can handle the astronomical connectedness of human-level intelligence. Nothing else can come close.

Louis Savain

Why Software Is Bad and What We Can Do to Fix It:

Before the Wright Brothers, the only flying things we knew of had flapping wings.

Biological systems have limitations that engineered systems do not. The wheel never evolved in a biological system because there is no way to get blood and nerves to a spinning wheel. Birds and insects flap their wings because, lacking propellers or turbines, they use their wings to generate both lift and thrust. But airplanes don't have that constraint, and no airplane was successful till the "flapping" was abandoned.

The human brain uses astronomical connectedness, but that doesn't mean that is the only way to acheive human-level intelligence. That may be the only way to do it within the limitations of biological neurons, but semiconductors have very different limitations. Neurons only switch 10 to 100 times per second. Transistors can switch billions of times per second, so maybe far, far fewer of them are necessary. A biological brain has to be fault tolerant, since thousands of neurons die everyday. Transistors are vastly more reliable than individual neurons. A biological brain has to self-assemble, heal itself, and do a lot of other basic biological activities that a computer does not have to worry about.

Also, biological brains are limited to what evolution can produce. Which means it can creep from one local maxima design to the next, but cannot make dramatic changes. That may work okay for a neural network, but you can't produce a good symbolic algorithm by random evolution "jiggling". Engineers can produce things that would never emerge from evolution.

To the contrary, I don't know of anything that an artificial neural network can do more efficiently than a symbolic algorithm.

It seems to me that the main argument for neural networks is "AI is really hard, so rather than actually trying to understand intelligence, let's just throw a bunch of artifical neurons into a network, and see if it just magically emerges."

I dont think there is a symbolic solution to the problem. Yes you can add endless pieces of information to your system, yes you can see excactly how its work. BUT when you add line after line to it, you expect the system to "magically" take over and become concious? Because that is what it takes for a symbolic system to become 'mister data'. I think neural networks are the answer, but not the traditional kind we are all familiar of.

This is a common but excruciatingly lame argument, IMO. All flying systems (flapping wings or rotating props) use the same aerodynamic principles. Same with any type of locomotion: they all use the same propulsion principle based on Newtonian action/reaction.

You're kidding me? You know of another way to create intelligence without making connections?

They don't need to do it faster than that.

We must have a huge amount of memory for human-level intelligence. It's all made of transistors,lots of them. What you should have said is that computers require far fewer processors because one processor can do the work of many neurons in the same amount of time.

So what? Nobody is claiming that we must simulate biological neurons down to their low-level bio-chemical processes. A neural network can certainly be created in software but, if you look into memory with a microscope, you're not going to see pathways and signals. You're going to see a bunch of transistors which are either on or off. That does not mean that the principles used in its operation and organization are not similar to those of biological networks. A software neural network is a virtual mechanism.

What is more dramatic than learning to walk, speak, drive a car around town and send people to the moon? It was all accomplished by the neural networks in our brains. You're purposely stupid or something?

ahahaha... This is really lame. The symbolic approach to AI is the work of the devil and those who promote it are children of the Devil (wake up! Minsky et al). ahahaha... It has been around for over half a century and it has been shown to be a pathetic failure. The entire symbol manipulation community should be tarred and feathered and paraded around town for having wasted humanity's time on a wild goose chase for half a century. ahahaha... Those who are still promoting this crap, at this late date in the con game, should be caned publically as an example to the younger generation. ahahaha...

I do. It's called the brain, artificial or otherwise.

You're using emotional buzz words like "throw" and "magically" for effect because you don't have a valid argument. You are revealing yourself to be nothing but a con artist (as always, I tell it like I see it). The fact that AI is hard is precisely why the symbolic approach is crap. And you know it. One cannot understand cognition because it is too complex for our limited brains to encompass. One can only understand its basic operating principles which is what the sensible AI researcher should be trying to figure out. One cannot program the astronomical interconnectedness of intelligence using a pure symbolic approach. It's pure stupidity.

Connectedness implies zillions of signals and connections between zillions of basic processors, which is another way of saying 'neural network'. Regardless of what the clueless symbolic AI fanatics ceaselessly preach, the proper goal of the AI scientist is to search for free lunches, i.e., to look for neural methods and principles that will allow intelligence to emerge automatically through learning and self-organizatioon. Any other approach is no better than pissing on a spark plug. ahahaha...

This is my last post on this thread. I'm really getting tired of this shit. I do it as public service but I think I should get paid from now on. ahahaha... Fire away!

Louis Savain

Why Software Is Bad and What We Can Do to Fix It:

Hot air ballons predate fixed wing aircraft by over a century. Kites are even older: it's not clear just when they were invented, in fact. The Wright Bros. plane was a powered kite. Attempts to power kites (and hot-air balloons) predate the Wright Bros. In fact, without those previous attempts, the Wrights couldn't have done it. Even their combination of a kite made with tension members for lightness plus a light motor wasn't the first one, either. There were dozens of attempts to use just this combination. But theirs worked well enough to say they were the first to do it successfully. They refined existing technology to the point of success, which is no small feat, especially when previous attempts could be seen as indications that it was dead end.

As for "flapping wings..." Well, that's a common misconception (oh, the awful eeffects of simplified school histories!) Have you ever actually watched birds? Or flying fish and flying squirrels, for that matter. Many birds glide - some spend more time glding than flapping their wings, in fact. Flying fish and squirrels glide. So do some insects, part of the time: eg, many butterflies alternate flapping and gliding flight. Even some bats glide some fo the time. FWIW, pterodactyls probably glided more than they flapped their wings. People knew long before the Wrights that flapping wasn't the only way to get into the air and move about in it.

HTH

And yet people still flap their mouths.

~v~~

That's old Star Trek, i.e., "Spock's Brain" episode. I'm talking Star Trek the Next Generation with the android Data.

Craig

Actually, if you get a chance, rent 'Saturn 3" with Kirk Douglas and Farrah Fawcett.

Very cool

Any neural network can be simulated using a symbolic algorithm.

Any symbolic algorithm can be simulated by arranging artifical neurons into NAND gates and building a turing machine out of them.

So anything a symbolic algorithm can do, a neural network can also do, and vice versa.

It is simply a question of which approach is more computationally efficient, and which is easier to implement. My opinion is that a symbolic algorithm is always more efficient, and for most problems, is easier to implement as well.

"Conciousness" is ill-defined and subjective. Besides, the goal is "intelligence", not conciousness. I do not expect it to magically emerge. I expect it to incremently improve.